Natural Language Processing and How it Could Improve Employee Engagement

Internal communication and employee engagement are key when it comes to the smooth functioning of an organization and building a reputation, especially in today’s age when more and more people are opting to work remotely and teams are scattered across the world.

Let’s face it like author and founder of LeadFactor Timothy R. Clark said, "Highly engaged employees make the customer experience. Disengaged employees break it."

It is the employees who build the company’s reputation. They are the voice and face of the company and their action has a great impact on the way that consumers perceive the products or services the company is providing.

Artificial intelligence has long been touted to have the potential to bridge the top-down communication gaps in organizations. Especially the use of Natural Language Processing to help empower computers to understand the language in a human way.

How does Natural Language Processing (NLP) work?

Although it is considered to be difficult to develop programs that can master the art of language processing, the use of NLP has brought us closer to developing machines that are able to accurately gauge human sentiments. 

Developers usually take into consideration two main factors to help machines understand the grammatical rules and sentiments in the spoken human language.

• Syntax
• Semantics

Syntax: Have you ever tried to use MS-Dos in your computer classes only to find that the minute you mistyped something the screen would read Syntax error? Which, basically meant that you used the wrong language.

The Syntax, in this case, is slightly different from the Syntax we are talking about when it comes to NLP as syntax basically means the arrangement of words in a sentence that makes grammatical sense. The computer algorithms are used to assess how the natural language coincides with the grammatical rules to derive meaning from them.

The Syntax techniques that are generally used in NLP include lemmatization, segmentation (morphological and word),  Part of speech tagging, parsing, sentence breaking and stemming.

Semantics: Semantic analysis refers to the meaning that has been conveyed in the conversation or text, in other words, the contextual meaning of what has been said. It involves applying computer algorithms to interpret the meanings of words and how sentences are structured. 

The Semantic techniques that are usually used involve Named Entity Recognition, Word Sense Disambiguation, and Natural Language Generation.

These factors and techniques are important because they allow computers to bypass the abstract and ambiguous characteristics of human communication.

How can NLP be used to bridge Top-Down Communication in Organizations

According to Gaurav Bhattacharya, the CEO of Involvesoft, a platform that enables companies to improve employee engagement through the use of NLP stated that it can greatly improve various aspects of internal communication inside companies.

“ It is a well-known fact that many companies fail to pass down relevant messages to their employees making them feel unwanted and disengaged,” said Bhattacharya.

According to him, NLP can be used to foster the employee-employer relationship by ensuring that the employees receive all the relevant messages that they require, whether it is through regular email messaging or through highlighting their accomplishments.

NLP platforms can enhance top-down communication in various ways, for instance, involve soft provides its users with Instagram-like feed that allows employees to read the latest news and announcements, spotlight stories, and take surveys and communicate with members of their community, which in turn improves top-down communication as the messages reach all the employees through the platform.

Bhattacharya believes that Natural Language Processing can bridge the communication gap as many people may feel comfortable finding out about things by asking chatbots, and through other mechanisms because they know that they will not be judged.

“ Everyone is different many times we backout when it comes to asking questions because we are worried our friends or colleagues may judge us, this has a negative impact on an employee’ a level of productivity as they will be too shy to ask and hence will continue making the same mistake or will avoid the work completely,” he said.

Incorporating NLP into your workplace

Before we incorporate AI into our startups it is very important to understand what it takes to engage employees.

“ I believe we should be able to attract someone’s interest, establish a meaningful connection with the employee and understand their strengths and weaknesses,” said Saumya Bhatnagar, the CTO of Involvesoft.

What else can it do for startups?

The use of AI assistants is not something new and it has been used for over a decade now. In a brief interview, Bhatnagar stated that any kind of conversation computation is imperative for startups

“ This is a data goldmine for any organization,” she said.

She also added that NLP and it’s processing capabilities to comb through the employee sentiments makes it a very important tool for human resource as well as marketing teams. 

According to Bhatnagar, startups can get actionable insights that can largely improve brand performance and marketing tactics.

“NLP creates a self-learning model that continues to evolve with every interaction. That means user experience, campaigning, advertising, etc can only get better at every turn, maximizing AI performance and results".